group_time: Grouping of intervals or events in time together
In epidm: UK Epidemiological Data Management

group_time

R Documentation

Grouping of intervals or events in time together

Description

Group across multiple observations of overlapping time intervals, with defined start and end dates, or events within a static/fixed or rolling window of time.

Usage

group_time(
  x,
  date_start,
  date_end,
  window,
  window_type = c("rolling", "static"),
  group_vars,
  indx_varname = "indx",
  min_varname = "date_min",
  max_varname = "date_max",
  .forceCopy = FALSE
)

Arguments

`x`	data frame, this will be converted to a data.table
`date_start`	column containing the start dates for the grouping, provided quoted
`date_end`	column containing the end dates for the interval, quoted
`window`	an integer representing a time window in days which will be applied to the start date for grouping events
`window_type`	character, to determine if a 'rolling' or 'static' grouping method should be used when grouping events
`group_vars`	in a vector, the all columns used to group records, quoted
`indx_varname`	a character string to set variable name for the index column which provides a grouping key; default is indx
`min_varname`	a character string to set variable name for the time period minimum
`max_varname`	a character string set variable name for the time period maximum
`.forceCopy`	default FALSE; TRUE will force data.table to take a copy instead of editing the data without reference

Value

the original data.frame as a data.table with the following new fields:

indx; renamed using indx_varname: an id field for the new aggregated events/intervals; note that where the date_start is NA, an indx value will also be NA
min_date; renamed using min_varname: the start date for the aggregated events/intervals
max_date; renamed using max_varname: the end date for the aggregated events/intervals

Examples

episode_test <- structure(
  list(
    pat_id = c(1L, 1L, 1L, 1L, 2L, 2L, 2L,
               1L, 1L, 1L, 1L, 2L, 2L, 2L),
    species = c(rep("E. coli",7),rep("K. pneumonia",7)),
    spec_type = c(rep("Blood",7),rep("Blood",4),rep("Sputum",3)),
    sp_date = structure(c(18262, 18263, 18281, 18282, 18262, 18263, 18281,
                          18265, 18270, 18281, 18283, 18259, 18260, 18281),
                        class = "Date")
  ),
  row.names = c(NA, -14L), class = "data.frame")

group_time(x=episode_test,
           date_start='sp_date',
           window=14,
           window_type = 'static',
           indx_varname = 'static_indx',
           group_vars=c('pat_id','species','spec_type'))[]

spell_test <- data.frame(
  id = c(rep(99,6),rep(88,4),rep(3,3)),
  provider = c("YXZ",rep("ZXY",5),rep("XYZ",4),rep("YZX",3)),
  spell_start = as.Date(
    c(
      "2020-03-01",
      "2020-07-07",
      "2020-02-08",
      "2020-04-28",
      "2020-03-15",
      "2020-07-01",
      "2020-01-01",
      "2020-01-12",
      "2019-12-25",
      "2020-03-28",
      "2020-01-01",
      rep(NA,2)
    )
  ),
  spell_end = as.Date(
    c(
      "2020-03-10",
      "2020-07-26",
      "2020-05-22",
      "2020-04-30",
      "2020-05-20",
      "2020-07-08",
      "2020-01-23",
      "2020-03-30",
      "2020-01-02",
      "2020-04-20",
      "2020-01-01",
      rep(NA,2)
    )
  )
)

group_time(x = spell_test,
           date_start = 'spell_start',
           date_end = 'spell_end',
           group_vars = c('id','provider'),
           indx_varname = 'spell_id',
           min_varname = 'spell_min_date',
           max_varname = 'spell_max_date')[]

epidm documentation built on July 12, 2022, 1:07 a.m.